



"""simple docstring"""






from copy import deepcopy



class     UpperCAmelCase    :



   def __init__(       self    :	Optional[Any]							,       __lowerCamelCase    :	list[int] | None = None							,       __lowerCamelCase    :	int | None = None				):



     """simple docstring"""

     if arr is None and size is not None:
       _snake_case   	=      size
       _snake_case   	=      [0] * size
     elif arr is not None:
       self.init(__lowerCamelCase				)
     else:
       raise ValueError('''Either arr or size must be specified'''				)



   def    __UpperCAmelCase    (       self    :	Any							,       __lowerCamelCase    :	list[int]				):



     """simple docstring"""
     _snake_case   	=      len(__lowerCamelCase				)
     _snake_case   	=      deepcopy(__lowerCamelCase				)
     for i in range(1							,       self.size				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         self.tree[j] += self.tree[i]



   def    __UpperCAmelCase    (       self    :	Union[str, Any]				):



     """simple docstring"""
     _snake_case   	=      self.tree[:]
     for i in range(self.size - 1							,       0							,       -1				):
       _snake_case   	=      self.next_(__lowerCamelCase				)
       if j < self.size:
         arr[j] -= arr[i]
     return arr



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index + (index & (-index))



   @staticmethod
   def    __UpperCAmelCase    (       __lowerCamelCase    :	int				):



     """simple docstring"""
     return index - (index & (-index))



   def    __UpperCAmelCase    (       self    :	Dict							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if index == 0:
       self.tree[0] += value
       return
     while index < self.size:
       self.tree[index] += value
       _snake_case   	=      self.next_(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     self.add(__lowerCamelCase							,       value - self.get(__lowerCamelCase				)				)



   def    __UpperCAmelCase    (       self    :	Optional[Any]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     if right == 0:
       return 0
     _snake_case   	=      self.tree[0]
     right -= 1  # make right inclusive
     while right > 0:
       result += self.tree[right]
       _snake_case   	=      self.prev(__lowerCamelCase				)
     return result



   def    __UpperCAmelCase    (       self    :	str							,       __lowerCamelCase    :	int							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.prefix(__lowerCamelCase				) - self.prefix(__lowerCamelCase				)



   def    __UpperCAmelCase    (       self    :	List[str]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     return self.query(__lowerCamelCase							,       index + 1				)





   def    __UpperCAmelCase    (       self    :	Optional[int]							,       __lowerCamelCase    :	int				):



     """simple docstring"""
     value -= self.tree[0]
     if value < 0:
       return -1

     _snake_case   	=      1  # Largest power of 2 <= size
     while j * 2 < self.size:
       j *= 2

     _snake_case   	=      0

     while j > 0:
       if i + j < self.size and self.tree[i + j] <= value:
         value -= self.tree[i + j]
         i += j
       j //= 2
     return i


if __name__ == "__main__":
     import doctest

     doctest.testmod()







"""simple docstring"""


import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()



class 	__a	(   _lowerCAmelCase     ,  _lowerCAmelCase     ,  unittest.TestCase						):
       UpperCamelCase_		:       Any				=      VQModel
       UpperCamelCase_		:       Dict				=      '''sample'''






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				List[str]=(32, 32)      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		4
              UpperCamelCase       			=		3

              UpperCamelCase       			=		floats_tensor((batch_size, num_channels) + sizes      ).to(UpperCAmelCase_      )

              return {"sample": image}






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				int      )->	List[Any]:







              """simple docstring"""
              return (3, 32, 32)






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Tuple:







              """simple docstring"""
              return (3, 32, 32)






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Optional[int]:







              """simple docstring"""
              UpperCamelCase       			=		{
                  "block_out_channels": [32, 64],
                  "in_channels": 3,
                  "out_channels": 3,
                  "down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
                  "up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
                  "latent_channels": 3,
              }
              UpperCamelCase       			=		self.dummy_input
              return init_dict, inputs_dict






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	List[Any]:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Any      )->	str:







              """simple docstring"""
              pass






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[int]      )->	List[Any]:







              """simple docstring"""
              UpperCamelCase			,							UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"							,   output_loading_info=UpperCAmelCase_      )
              self.assertIsNotNone(UpperCAmelCase_      )
              self.assertEqual(len(loading_info["missing_keys"]      )							,   0      )

              model.to(UpperCAmelCase_      )
              UpperCamelCase       			=		model(**self.dummy_input      )

              assert image is not None, "Make sure output is not None"






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]      )->	List[str]:







              """simple docstring"""
              UpperCamelCase       			=		VQModel.from_pretrained("fusing/vqgan-dummy"      )
              model.to(UpperCAmelCase_      ).eval()

              torch.manual_seed(0      )
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0      )

              UpperCamelCase       			=		torch.randn(1							,   model.config.in_channels							,   model.config.sample_size							,   model.config.sample_size      )
              UpperCamelCase       			=		image.to(UpperCAmelCase_      )
              with torch.no_grad():
                     UpperCamelCase       			=		model(UpperCAmelCase_      ).sample

              UpperCamelCase       			=		output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              UpperCamelCase       			=		torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]      )
              # fmt: on
              self.assertTrue(torch.allclose(UpperCAmelCase_							,   UpperCAmelCase_							,   atol=1e-3      )      )





